If you're someone who wants to use data, data infrastructure, data science, machine learning, and AI, we're really at the point where there are a lot of tools for implementers and developers. They're not necessarily doing research and development; they just want to build better products and automate workflow. I think that's the most significant development in my mind.
And then I think use case sharing also has an impact. For example, at our conferences, people are sharing how they're using AI and ML in their businesses, so the use cases are getting better defined-particularly for some of these technologies that are relatively new to the broader data community, like deep learning. There are now use cases that touch the types of problems people normally tackle-so, things that involve structured data, for example, for time series forecasting, or recommenders.
With that said, while we are in an implementation phase, I think as people who follow this space will attest, there's still a lot of interesting things coming out of the R&D world, so still a lot of great innovation and a lot more growth in terms of how sophisticated and how easy to use these technologies will be.
Addressing ML and AI bottlenecks
We have a couple of surveys that we'll release early in 2019. In one of these surveys, we asked people what the main bottleneck is in terms of adopting machine learning and AI technologies.
Interestingly enough, the main bottleneck was cultural issues-people are still facing challenges in terms of convincing people within their companies to adopt these technologies. And then, of course, the next two are the ones we're familiar with: lack of data and lack of skilled people. And then the fourth bottleneck people cited was trouble identifying business use cases.
What's interesting about that is, if you then ask people how mature their practice is and you look at the people with the most mature AI and machine learning practices, they still cite a lack of data as the main bottleneck. What that tells me is that there's still a lot of opportunities for people to apply these technologies within their companies, but there's a lot of foundational work people have to do in terms of just getting data in place, getting data collected and ready for analytics.
Focus on foundational technologies
At the Strata Data conferences in San Francisco, London, and New York, the emphasis will be building technologies, bringing in technologies and cultural practices that will allow you to sustain analytics and machine learning in your organization. That means having all of the foundational technologies in place-data ingestion, data governance, ETL, data lineage, data science platform, metadata, store, and things like that, the various pieces of technology that will be important as you scale the practice of machine learning and AI in your company.
At the Artificial Intelligence conferences, we remain focused on being the de facto gathering place for people interested in applied artificial intelligence. We will focus on servicing the most important use cases in many, many domains. That means showcasing, of course, the latest research in deep learning and other branches of machine learning, but also helping people grapple with some of the other important considerations, like privacy and security, fairness, reliability, and safety.
At both the Strata Data and Artificial Intelligence conferences, we will focus on helping people understand the capabilities of the technology, the strengths, and limitations; that's why we run executive briefings at all of these events. We showcase case studies that are aimed at the non-technical and business user as well so, we'll have two types of case studies, one more technical and one not so technical so the business decision-makers can benefit from seeing how their peers are using and succeeding with some of these technologies.